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name: labeling description: "Operate Snorkel labeling functions, appliers, LF analysis, and label models." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Labeling

Use this sub-skill for Snorkel weak-supervision labeling work:

• authoring LabelingFunction objects and labeling_function decorators
• attaching small resources dictionaries and local pre preprocessors to LFs
• applying LFs to lists, NumPy arrays, Pandas DataFrames, Dask DataFrames, and Spark RDDs
• inspecting label matrices with LFAnalysis
• training and scoring LabelModel
• using RandomVoter, MajorityClassVoter, and MajorityLabelVoter
• filtering training rows with filter_unlabeled_dataframe
• using spaCy-based NLPLabelingFunction and spark_nlp_labeling_function

Do not use this sub-skill for:

• generic mapper / preprocessor / transformation mechanics → ../data-transforms/SKILL.md
• downstream task, trainer, or classifier work → ../classification/SKILL.md
• slicing functions or slice-aware models → ../slicing/SKILL.md

Fast workflow

1. Define each LF and keep names unique.
2. Add only lightweight local preprocessors and resources that belong to the LF.
3. Apply the LFs with the right applier for the input shape.
4. Run LFAnalysis before training any label model.
5. Fit LabelModel only after the matrix has meaningful coverage and at least 3 LFs.
6. Use predict_proba, predict, or a voter baseline to generate weak labels.
7. Filter out unlabeled rows before handing probabilities to a downstream classifier.

Common routing rules

• Need a reusable data mapper or preprocessor pipeline? Use data-transforms.
• Need Task, Trainer, or weak-label-to-discriminative-model training? Use classification.
• Need slice functions or SliceAwareClassifier? Use slicing.
• Need Dask, spaCy, or Spark setup troubleshooting? See references/troubleshooting.md.

Runtime aids

• API details: references/api-reference.md
• End-to-end recipes: references/workflows.md
• Troubleshooting guide: references/troubleshooting.md
• CPU smoke check: scripts/labeling_smoke.py
• Optional local Spark smoke: scripts/labeling_spark_smoke.py

Operational hints

• LFs should return integer labels and use -1 only for abstain.
• fault_tolerant=True on an applier turns LF failures into abstains and records fault counts in metadata.
• LabelModel needs a label matrix with at least 3 LFs and labels in [-1, 0, ..., cardinality-1].
• spaCy NLP helpers default to en_core_web_sm and memoization is enabled by default for NLPLabelingFunction.
• Local Spark use is optional; the Spark smoke script is only for environments with Java and PySpark.